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short-paper

A Novel Approach for Classifying Gene Expression Data using Topic Modeling

Authors:

Hima Bindu Yalamanchili,

Michael L. Raymer,

Amit P. ShethAuthors Info & Claims

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

Pages 388 - 393

https://doi.org/10.1145/3107411.3107483

Published: 20 August 2017 Publication History

Abstract

Understanding the role of differential gene expression in cancer etiology and cellular process is a complex problem that continues to pose a challenge due to sheer number of genes and inter-related biological processes involved. In this paper, we employ an unsupervised topic model, Latent Dirichlet Allocation (LDA) to mitigate overfitting of high-dimensionality gene expression data and to facilitate understanding of the associated pathways. LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. Here, we proposed to use LDA in clustering as well as in classification of cancer and healthy tissues using lung cancer and breast cancer messenger RNA (mRNA) sequencing data. We describe our study in three phases: clustering, classification, and gene interpretation. First, LDA is used as a clustering algorithm to group the data in an unsupervised manner. Next we developed a novel LDA-based classification approach to classify unknown samples based on similarity of co-expression patterns. Evaluation to assess the effectiveness of this approach shows that LDA can achieve high accuracy compared to alternative approaches. Lastly, we present a functional analysis of the genes identified using a novel topic profile matrix formulation. This analysis identified several genes and pathways that could potentially be involved in differentiating tumor samples from normal. Overall, our results project LDA as a promising approach for classification of tissue types based on gene expression data in cancer studies.

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Cited By

Srivastava NTayal D(2024)SVAD: Stacked Variational Autoencoder Deep Neural Network-Based Dimensionality Reduction and Classification of Small Sample Size and High Dimensional DataSN Computer Science10.1007/s42979-024-03294-25:7Online publication date: 12-Oct-2024
https://doi.org/10.1007/s42979-024-03294-2
Abdelrazek AEid YGawish EMedhat WHassan A(2023)Topic modeling algorithms and applicationsInformation Systems10.1016/j.is.2022.102131112:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.is.2022.102131
Madhukar Rao GRamesh D(2023)A Novel Approach for Visualizing Medical Big Data Using Variational AutoencodersICDSMLA 202110.1007/978-981-19-5936-3_31(337-346)Online publication date: 7-Feb-2023
https://doi.org/10.1007/978-981-19-5936-3_31
Show More Cited By

Index Terms

A Novel Approach for Classifying Gene Expression Data using Topic Modeling
1. Applied computing
  1. Life and medical sciences
    1. Bioinformatics
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

August 2017

800 pages

ISBN:9781450347228

DOI:10.1145/3107411

General Chairs:
Nurit Haspel
University of Massachusetts Boston, USA
,
Lenore J. Cowen
Tufts University, USA
,
Program Chairs:
Amarda Shehu
George Mason University, USA
,
Tamer Kahveci
University of Florida, USA
,
Giuseppe Pozzi
Politecnico di Milano, Italy

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 August 2017

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BCB '17

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SIGBio

BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 20 - 23, 2017

Massachusetts, Boston, USA

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ACM-BCB '17 Paper Acceptance Rate 42 of 132 submissions, 32%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Srivastava NTayal D(2024)SVAD: Stacked Variational Autoencoder Deep Neural Network-Based Dimensionality Reduction and Classification of Small Sample Size and High Dimensional DataSN Computer Science10.1007/s42979-024-03294-25:7Online publication date: 12-Oct-2024
https://doi.org/10.1007/s42979-024-03294-2
Abdelrazek AEid YGawish EMedhat WHassan A(2023)Topic modeling algorithms and applicationsInformation Systems10.1016/j.is.2022.102131112:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.is.2022.102131
Madhukar Rao GRamesh D(2023)A Novel Approach for Visualizing Medical Big Data Using Variational AutoencodersICDSMLA 202110.1007/978-981-19-5936-3_31(337-346)Online publication date: 7-Feb-2023
https://doi.org/10.1007/978-981-19-5936-3_31
Madhavan MStephen RGopakumar G(2020)Prediction of lncRNA-Cancer Association Using Topic Model on GraphsAdvances in Machine Learning and Computational Intelligence10.1007/978-981-15-5243-4_28(311-319)Online publication date: 26-Jul-2020
https://doi.org/10.1007/978-981-15-5243-4_28
Mahmud MFu XHuang JMasud M(2019)High-Dimensional Limited-Sample Biomedical Data Classification Using Variational AutoencoderAlpha-Synuclein10.1007/978-981-13-6661-1_3(30-42)Online publication date: 16-Feb-2019
https://doi.org/10.1007/978-981-13-6661-1_3
Qiao CHu X(2018)Discovering Student Behavior Patterns from Event Logs: Preliminary Results on a Novel Probabilistic Latent Variable Model2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT.2018.00056(207-211)Online publication date: Jul-2018
https://doi.org/10.1109/ICALT.2018.00056
Yalamanchili HKho SRaymer M(2017)Latent Dirichlet Allocation for Classification using Gene Expression Data2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE)10.1109/BIBE.2017.00-81(39-44)Online publication date: Oct-2017
https://doi.org/10.1109/BIBE.2017.00-81

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